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The Causal Implications of Mechanistic Thinking: Identification Using Directed Acyclic Graphs (DAGs)

  • Carly R. KnightEmail author
  • Christopher Winship
Chapter
Part of the Handbooks of Sociology and Social Research book series (HSSR)

Abstract

In analyzing causal claims, the most common evidentiary strategy is to use an experimental or quasi-experimental framework; holding all else constant, a treatment is varied and its effect on the outcome is determined. However, a second, quite distinct strategy is gaining prominence within the social sciences. Rather than mimic an experiment, researchers can identify causal relations by finding evidence for mechanisms that link cause and effect. In this chapter, we use Directed Acyclic Graphs (DAGs) to illustrate the power of using mechanisms. We show how mechanisms can aid in causal analysis by bringing additional variation to bear in instances where causal effects would otherwise not be identified. Specifically, we examine five generic situations where a focus on mechanisms using DAGs allows an analyst to warrant causal claims.

Keywords

Causal Effect Directed Acyclic Graph Causal Mechanism Causal Chain Causal Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors wish to thank Steve Morgan, Judea Pearl, Tyler VanderWeele, Chris Muller, and Joe Krupnick for helpful comments. Of course, any remaining errors are the authors’ responsibility.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of SociologyHarvard UniversityCambridgeUSA

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